Metadata standards enable the uniform description of similar data using metadataMetadata are descriptions of research data (data about data) and provide content-related and structured information about the research context, methodological and analytical procedures, as well as the research team that generated the data. They can be categorized into bibliographic, administrative, procedural, and descriptive metadata and are typically created using templates, ReadMe files, or data curation profiles. Metadata are published alongside the research data themselves and are essential in online repositories and research data centers, where they enable third parties to understand and contextualize datasets. Metadata also enhances the findability and machine-readability of data, making them a key component of the FAIR Principles and good scientific practice. Read More. They establish terms, meanings, structure, and format as standards for specific disciplines, for example. Metadata standards enhance data discoverability and support interoperabilityInteroperability is the ability of a system to work seamlessly with other systems. In interoperable systems, data can be automatically combined and exchanged with other datasets, making data machine-readable, interpretable, and comparable in a simplified and accelerated manner. Interoperability is one of the main criteria of the FAIR Principles (Forschungsdaten.info, 2023). Read More between applications, thereby facilitating data exchange, comparison, and linkage across datasets¹(Have a look at the following example: https://www.dublincore.org/specifications/dublin-core/dcmi-terms/)..